Early studies of gene expression in single cells revealed a greater than expected amount of heterogeneity among cells from supposedly homogeneous populations. It is now well accepted that, for example, not every tumor cell, liver cell, or stem cell isolated from a distinct population will be identical in terms of which genes are expressed and at what levels.

Simply averaging the transcription levels measured across multiple individual cells will not accurately convey what is taking place in any one cell. Furthermore, this approach may result in an especially interesting and biologically or clinically relevant characteristic of one or more of the cell subtypes present in a cell population to be missed if their genomic heterogeneity is not analyzed and understood.

The desire to characterize this variability in gene expression across cells and to acquire genomic sequence data on a single-cell level is driving advances in the field of single-cell genomics, which is part of a broader field of single-cell analysis that is benefiting from an emerging trend to develop and apply tools and technologies for robust, high-throughput, and reproducible analysis of biological function at the single-cell level.

Initial focus areas include developmental biology and cancer and stem cell research; early commercial applications relate to embryo screening for in vitro fertilization (IVF) procedures, characterization and drug-response testing of circulating tumor cells, and infectious-disease diagnostics. Single-cell genomic analysis was a key area of discussion at Select Biosciences’ “Single Cell Analysis Summit” held recently in San Diego.

In reality, “most cells have very few transcripts, and a few cells have a lot of gene expression,” explained Mikael Kubista, Ph.D., CEO of Tataa Biocenter. Gene expression is a highly dynamic process, occurring in bursts of active transcription followed by less active periods during which existing mRNA transcripts decay.

“We have found that these bursts generally do not correlate from cell to cell,” Dr. Kubista said. This led the company to develop its single-cell transcription correlation platform, in which identification of a cell type is not based on the level of any particular transcript, but rather on correlations of transcription.

Using single-cell expression correlation, it is possible to distinguish between two or more subtypes of cells within a population that express the same transcripts. They differ not in the presence or absence of a particular transcript, but in the pattern of gene-expression levels. One way Tataa is applying this technology is to study circulating tumor cells, with a clinical goal of identifying what clones are present in a patient’s blood and to which chemotherapeutic agents they may/may not respond.

Tataa developed a technique for measuring intracellular mRNA gradients using qPCR and applies it to the study of gene-expression heterogeneity at the single-cell and subcellular level. To capture mRNA transcripts from a single cell without significant loss of material, Tataa formulated a set of detergents to facilitate cell lysis and mRNA removal without the need for washing steps. It licensed the detergents to Roche, which incorporated them into its RealTime Ready Cell Lysis Kit.

Dr. Kubista identified three main challenges at present for single-cell gene-expression analysis including the need for robust single-cell isolation techniques, increased throughput, and improved tools for data mining and multivariate data analysis.

Steven Bodovitz, Ph.D., principal at Bioperspectives, defined the opportunity in this field as the ability to “transform cellular heterogeneity from a source of noise into a source of new discoveries.”

The reasons to do so are “compelling,” he said. For example, if an easy and more reliable method was available to identify the different cell subtypes in a tumor, could a more effective, multitargeted chemotherapeutic approach be developed?

Dr. Bodovitz identified two main drivers of single-cell omics research: the potential biological significance of understanding cell heterogeneity, and the enabling technological advances including miniaturization, microfluidics, and whole-genome amplification (WGA) techniques that yield enough DNA from a single cell to enable genomic analysis using available gene-expression analysis or next-generation sequencing (NGS) methods.

When asked to identify the main challenge the field of single-cell genomics currently faces, Dr. Bodovitz pointed to the need for improved methods of isolating single cells from tissue samples. “One tends to destroy the cell to analyze it.”

Whereas perturbations to a cell are unlikely to affect the results of genetic or epigenetic analysis, as these characteristics should remain stable, cellular disruption could affect gene-expression analysis and other types of omics studies. Intentional perturbation of a single cell represents an “elegant system” for studying biological pathways and intracellular networks based on the up- or downregulation of gene expression, added Dr. Bodovitz. This knowledge could be used to guide the design of drugs capable of interfering in a particular pathway.

Single-Cell Sequencing

John Langmore, Ph.D., vp of commercial development at Rubicon Genomics, focused his presentation on methods designed to streamline the preparation of DNA derived from single cells for NGS for copy-number variation, mutation, and DNA-methylation analysis.

The analytical robustness, reproducibility, and accuracy of current genomic analysis techniques including qPCR, microarrays, and NGS are sufficient for many cancer-research applications and could serve as the basis for diagnostic and prognostic tests performed at the single-cell level, according to Dr. Langmore.

Rubicon introduced its single-cell PicoPlex™ whole-genome amplification kits for genetic analysis using PCR and microarrays in June 2009. In November of this year, Rubicon initiated beta testing of its new PicoPlex-NGS WGA kit optimized for next-generation sequencing, and in January the company plans to release PicoPlex-NGS whole methylome amplification (WMA) kits for methylation analysis using NGS.

Earlier this year, the European Society for Human Reproduction and Embryology presented the results of a clinical study showing that a method utilizing Rubicon’s PicoPlex WGA kits and BlueGnome’s 24sure™ microarrays for diagnosing genetic abnormalities in human eggs in advance of IVF, called preimplantation genetic screening (PGS), could detect chromosomal abnormalities accurately 89% of the time.

WGA was used to amplify the DNA present in polar bodies, which are small particles formed as a by-product of egg development. Analysis of the amplified DNA is used to infer whether the DNA remaining in the egg is euploid or aneuploid (having normal or abnormal chromosome number and structure, respectively).

Rubicon designed its WGA kits to meet the needs of multiple markets, including determining which embryo(s) to implant during IVF using PGS to identify chromosomal abnormalities and preimplantation genetic diagnostics (PGD) to determine the presence/absence of a hereditary abnormality known to be carried by one or both parents, as well as the cancer and stem cell research markets.

The PGS and PGD fields stand to benefit from a large-scale prospective clinical trial planned in Europe that is designed to determine whether the application of a method for selecting healthy embryos before implantation will result in an increased number of live births. These findings could have economic implications as well, with the potential for implanting fewer embryos and reducing the rate of multiple births. At present, according to Dr. Langmore, only about 2% of the 1 million or so cases of IVF performed worldwide each year undergo PGS and PGD.

In the prenatal testing arena, robust single-cell analytical techniques could lead to diagnostic applications using fetal cells isolated from the maternal bloodstream or short free fetal DNA fragments present in the maternal circulation or urine.

Tools for Single-Cell Analysis

Ken Livak, Ph.D., a distinguished scientific fellow at Fluidigm, described how to derive cell signatures using the company’s Dynamic Array™ chips and BioMark™ system. While it is still quite costly to do whole-transcriptome analysis in a discovery or exploratory mode, time- and cost-efficient methods are available to analyze gene expression in subsets of genes.

To get meaningful results, one needs to collect data from a sufficient number of single cells to overcome the relatively high level of biological noise in the system, and so throughput and miniaturization become important factors, explained Dr. Livak. Looking at 100 genes in 100 cells, for example, requires 10,000 experiments.

Working with single cells means working at microvolume scale, and he described an emerging trend toward developing entire workflows optimized for processing at microvolume scale—from cell isolation to sample preparation to gene-expression analysis.

Dr. Livak’s talk focused mainly on how to analyze the data generated from gene-expression profiling of single cells. This is an evolving field, he noted, with no standard approach to data analysis. The goal is to identify correlations in transcript levels across a set of genes.

Because there is a high level of variation in the expression of any one gene and transcript levels are best represented by a log-normal distribution, one cannot normalize the expression data to those of housekeeping genes, as might be done in a conventional qPCR-based study. Instead, explained Dr. Livak, transcript levels in individual cells are normalized to the median of the distribution, thereby taking into account the fact that different genes will represent the median in different cells.

In his presentation, Dr. Livak referred to a study conducted by Paul Robson and colleagues at the National University of Singapore and the Genome Institute of Singapore to identify regulatory networks and developmental mechanisms that control cell fate decisions published in Developmental Cell in 2010. In initial studies to explore the expression of cell-type-specific transcription factors in the 64-cell stage mouse blastocyst, the researchers generated data from pools of cells. “As cell fate decisions are made by individual cells, this averaged expression may mask interesting single-cell dynamics,” the authors stated.

In the subsequent study described in the paper, they analyzed gene expression at the single-cell level and looked for correlations in the expression of multiple genes to identify cell signatures that correlate to an embryo transitioning from a 1- to 64-cell state. They disrupted each embryo into 64 individual cells and surveyed 48 genes across more than 500 cells using the Fluidigm Dynamic Array 48.48 and the BioMark system. Their results included the identification of two genes, ID2 and SOX2, which are the earliest markers of outer and inner cell populations, respectively.

According to Fluidigm, the BioMark System is capable of supporting a variety of applications, including digital PCR, SNP genotyping, and gene expression, all on the same instrument. Users just have to select the appropriate chip.

Richard Fekete, a senior manager at Life Technologies, described the use of the company’s Ambion® Single Cell-to-CT™ kit and OpenArray® real-time qPCR technologies for gene-expression analysis.

With traditional sample-prep workflows, “we noticed that when you purify RNA or DNA from a small number of cells, it tends to bind irreversibly to the matrix, or it does not bind at all,” he said. Life Technologies designed the Cell-to-CT kit to perform sample prep (including cell lysis and genomic DNA removal), reverse transcription, and preamplification in a single tube to avoid loss of material that can result from sample transfer and processing.

Tracing the history of single-cell analysis at the company, Fekete described early work with preamplification and qPCR techniques that allowed for the analysis of RNA from a small number of cells. “We saw quite variable results from single cells suggesting a lot of cell-to-cell differences,” remarked Fekete.

Life Technologies boasts a simple and streamlined approach to single-cell analysis that uses a limited number of reagents and no tube transfers. The workflow enables customers to investigate up to 100 genes from hundreds of single-cell samples, while taking less than five hours to complete.